We noticed that for some combinations of hyper-parameters and datasets, the learning process for the Bounded domain model becomes unstable. Points eventually fall outside of the space, and need to be projected in every epoch.
We did not observe this behavior on the Siegel model. We consider that these findings are in line with the ones reported on [1]}, where they observe that the Lorentz model, since it is unbounded, is more stable for gradient-based optimization than the Poincaré one.
